Identifying multiple resources to perform a service request

ABSTRACT

Examples herein disclose receiving a service request including a latency associated with a publication of a result of the service request. The examples disclose computing a workload for the service request and identifying multiple resources which are to perform the service request by applying the latency to the multiple resources.

BACKGROUND

Heterogeneous data is data from a multiple number of sources which maybe unknown and unlimited in various formats. Heterogeneous data providesa mechanism to refer to data that may be of unknown format and/orunknown content.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings, like numerals refer to like components orblocks. The following detailed description references the drawings,wherein:

FIG. 1 is a block diagram of an example system including a servicerequestor to submit a service request including a latency to ascheduling module, the scheduling module computes a workload tot aresource profiler to identify multiple resources to perform the servicerequest;

FIG. 2A is a block diagram of an example computing system including aservice requestor to submit a service request to a scheduling module,the scheduling module retrieves heterogeneous data from a data storagearea for computing a workload for input to a resource profiler thatidentifies multiple resources to perform the service request accordingto the workload and within a latency included within the servicerequest;

FIG. 2B is a block diagram of an example service request from a servicerequestor the service request includes a job request and a latency ofthe request;

FIG. 2C is a block diagram of an example workload as input to convertinto a resource profile;

FIG. 3 is a flowchart of an example method to receive a service requestincluding a latency and computing a workload for performing the servicerequest, the method identifies multiple resources to perform the servicerequest and publishes a result of the service request;

FIG. 4 is a flowchart of an example method to identify multipleresources for performing a service request by mapping a workload tomultiple resources and applying a latency to the multiple resources foridentifying the multiple resources which are to perform the workload;

FIG. 5 is flowchart of an example method to receive a service requestincluding a latency and identifying multiple resources to perform theservice request by representing a workload as a vector to obtain aresource profile and applying the latency to the resource profile; and

FIG. 6 is a block diagram of an example computing device with aprocessor to execute instructions in a machine-readable storage medium,for receiving a service request specifying a latency and publishingresults of the service request in accordance with the latency.

DETAILED DESCRIPTION

Heterogeneous data may be collected from an unknown number of sourcesand in an unknown number of formats. Heterogeneous data may be managedand processed to complete job requests; however, these job requests maynot be managed in accordance for estimating a world cad for the requestand when the request may be completed. As such, this may decrease thequality of completing the job. For example, if a time to perform therequest takes longer than a specified request, this may lower quality ofservice. Additionally, completing the workload maybe be limited due tocomputing capability of resources which may perform the workload.Further, this may cause issues for scheduling such job requests as timemay be of the essence.

To address these issues, examples disclosed herein provide an efficientmechanism for adjusting a workload for a query and/or job request basedon a maximum latency and the computing capability of multiple resources.Based on the workload, the examples may identify which resources are toperform the workload to ensure work is performed within a specifiedperiod of time.

The examples disclose receiving a service request which includes the jobrequest and/or query and specifies the latency in which to perform theservice request. The latency is a requirement pre-defined by a servicerequestor. The latency is a period of time in which the servicerequester requests results of the query based on the submission of theservice request. For example, if the latency is five minutes, uponreceiving the service request, the examples perform the service requestand returns results within five minutes. In this context, the latencyserves as an upper bound of time in which to publish results.Additionally, in this context, the latency is considered associated withthe publication and/or delivery of the results of the service request.

Additionally, the computing system identifies multiple resources basedon the latency and the workload to perform the service request.Identifying the multiple resources enables the computing system to takeinto account various computing capabilities, such as computing capacityand computing availability of each of the resources. For example, oneresource may have a double processing core and thus may be able toprocess more data than a resource with a single processing core. Thisenables the computing system to take into account the computingcapabilities of the resources to ensure results of the service requestare published within the specified latency. Further, this enables thecomputing system to adjust the number, type and/or combination of theresources to ensure the service request is performed within the latencyperiod.

Identifying which multiple resources are to perform the workloadcorresponding to the service request, enables the computing system toadjust the workload performed by each of the multiple resources formeeting the latency requirement. Such adjustment, in addition to themeeting the latency requirement, serves to optimize a business objectiveof the computing system. For example, the business objective may includeminimizing a total cost of the resources for a particular servicerequest.

In another example discussed herein, the resources are allocated and/oridentified in a manner optimized to fulfill the service requestefficiently within the latency period. This optimization takes intoaccount variable conditions include a quantity of relevant data and arelationship of a quantity of the resources versus the latency period.

Thus, examples disclosed herein provide an efficient mechanism tobalance and adjust workloads to various resources to ensure aperformance of a service request within specified latency.

Referring now to the figures, FIG. 1 is a block diagram of an examplecomputing system including a service requestor 102 to submit a servicerequest 104 to a scheduling module 108. The service request 102 includesa latency 106 which specifies a maximum amount of time in which thecomputing system may publish results of the service request to theservice requestor 102. Upon receiving, the service request 104, thescheduling module 108 computes a workload at module 110 for quantifyinga workload 112 for performance of the service request 104. Uponreceiving the quantified workload 112, a resource profiler 114determines a workload capability of each multiple resource at module116. From determining the workload capability at module 116, theresource profiler 114 proceeds to module 118 for identifying themultiple resources for completing the service request within thelatency. The computing system in FIG. 1 represents a cloud computingtype of system for delivery of a computing service rather than aproduct. In this example, the components within FIG. 1 may be shared andprovided among computing devices within a network. In anotherimplementation the computing system in FIG. 1 represents a virtualmachine to which the service requester 102 may submit the servicerequest 104 for a particular job query. Additionally, the dottedhorizontal lines within FIG. 1 represent the various layers of thecomputing system. For example, the scheduling module 108 and theresource profiler 114 may be included as part of a data processinglayer, while another layer may include a data storage area, dataanalysis area, etc. This may be explained in a later figure.

The service requestor 102 represents an entity from over the network ora local entity which submits the service request 104. The servicerequester 102 may include an individual, service, and/or machine whichmay desire certain results out of the computing system. Thus, theservice requester 102 may drive data analysis through the submission ofthe service request 104. Although FIG. 1 illustrates the servicerequestor 102 as part of the computing system, this was done forillustration purposes as the service requestor 102 may be separateand/or independent of the computing system in FIG. 1.

The service request 104 is a request submitted by the service requester102 over the network, As such, the service request 104 may be submittedusing JavaScript Object Notation (JSON), extensible markup language(XML), or other type of format enabling the computing system to read theservice request 104. The service request 104 includes a job requestincluding a query specifying information about a particular componentand the latency 106. For example, the job request may requestinformation about when a printer may run out of ink and/or paper, etc.The latency 106 is defined as period of time between when the servicerequest is submitted and the service requester 102 receives results ofthe service request 104. In this manner, the latency 106 is consideredassociated with the publication and/or delivery of the results of theservice request 104 to the service requester 102. Additionally, theservice request 104 may provide a filtering scheme in which to processthe retrieved data for obtaining the data relevant to the servicerequest 104.

The latency 106 is a requirement pre-defined by the service requester102 as to when to publish results based on the submission of the servicerequest 104. The latency 106 represents an upper time limit in which theuser try desire results of the completed the service request 104;however, the results may also be published earlier than the latency 106specification. For example, if the latency 106 includes 5 minutes, theresults may be published in 5 minutes and under. In one implementation,upon receiving the service request 104 including the latency 106, thecomputing system creates a clock signal corresponding to the latency106. In this implementation, the pre-defined latency 106 may includetime intervals defining how often to publish results of the servicerequest 104. For example, in this implementation, the clock signalindicates for the scheduling module 108 to retrieve data from the datastorage area. Upon retrieving the data, the data may be filtered toobtain the data relevant to the service request 104. This implementationmay be discussed in detail in a later figure. In another implementation,the latency 106 includes a frequency corresponding to how often resultsof the service request 104 are published to the service requestor 102.

The scheduling module 108 receives the service request 104 and as suchcomputes the workload at module 110 for performing the query ofinformation in the service request 104. In one implementation, thescheduling module 108 retrieves data from a data storage area within thecomputing system and filters the data to obtain data relevant to theservice request 104. Implementations of the scheduling module 108include by way of example, a virtual machine, controller, processingunit, instruction, set of instructions, process, operation, logic,technique, function, firmware, and/or software executable by aprocessing component (not illustrated) within the computing system inFIG, 1.

At module 110, the scheduling module 108 computes the workload forperforming the service request 104. Computing the workload at module 110includes obtaining a quantified expression for the workload 112 in sucha manner that the resource profiler 114 is able to identify the multipleresources which can carry out the workload 112 for performing theservice request 104. As such the quantified expression may include, byway of example, a single value, vector of values and/or other type ofcombination of values. Each value in the vector quantifies one aspect ofthe workload 112. Additionally, computing the workload at module 110includes, by way of example, retrieving and filtering data to obtaindata relevant to the service request 104. Thus, module 110 may includedetermining an amount of the relevant data, types of relevant data,determining a number of resources for computing the workload,determining a number of resources for performing the job in the servicerequest 104, etc. Implementations of the module 110 include, by way ofexample, an instruction, set of instructions, process, operation, logic,technique, function, firmware, and/or software executable by aprocessing component (not illustrated) within the computing system inFIG. 1.

The workload 112, is the quantified amount of the workload computed atmodule 110. The resource profiler 114 uses the workload 112 as input tomap the workload to various resources which may perform the servicerequest 104. In one implementation, the workload 112 is converted into aresource profile which includes data points. The data points represent arelationship of a quantity and/or type of resources versus the latency.For example, the relationship may be expressed as a graph and/or chartrepresenting the resources and various latencies. In anotherimplementation, the workload 112 is expressed as a vector based on therelevant data obtained by the scheduling module 108. Theseimplementations may be discussed in later figures.

The resource profiler 114 uses the workload 112 and at modules 116-118determines the workload capability of each of the multiple resources andas such identifies those multiple resources which are to perform theservice request 104. Determining the workload capability at module 116enables the resource profiler 114 to adjust the workload performed byeach of the multiple resources for meeting the latency 106 in theservice request 104. Such adjustment, in addition to the meet thelatency 106 requirement, also serves to optimize a business objective ofthe computing system. For example, minimize total cost of computingresources for a particular service request. Each of the multipleresources have a different computing capabilities, such as an amountand/or time in which the resources may process data. For example, oneresource may have multiple processing cores, thus enabling this resourceto process more relevant data than another resource which may have asingle processing core. Implementations of the resource profiler 114include, by way of example, a virtual machine, controller, processingunit, instruction, set of instructions, process, operation, logic,technique, function, firmware, and/or software executable by aprocessing component (not illustrated) within the computing system inFIG. 1.

At modules 116-118, upon receiving the workload 112, the resourceprofiler 114 determines the workload capability of each resource amongmultiple resources. Using the workload capability of each of theresources at module 116 enables the resource profiler 114 to identifythe multiple resources which are each to carry out a different workloadfor performing the service request 104. Implementations of modules116-118 include, by way of example, an instruction, set of instructions,process, operation, logic, technique, function, firmware, and/orsoftware executable by the processing component (not illustrated) withinthe computing system in FIG. 1.

FIGS. 2A-2C illustrate an example computing system to receive a servicerequest including a job request and a latency in which to perform thejob request. Additionally, FIG. 2C illustrates a workload as input forconverting into a resource profile.

FIG. 2A is a block diagram of an example computing system including aservice requestor 102 to submit a service request 104 including alatency 106 to a scheduling module 108. The scheduling module 108retrieves heterogeneous data 224 from a data storage area 224 forfiltering the heterogeneous data 224 to obtain data relevant to theservice request 104. Upon obtaining the relevant data, the schedulingmodule 108 computes a workload at module 110. Computing the workload atmodule 110 provides a quantification of a workload 112 as input to aresource profiler 114. The resource profiler 114 determines workloadcapability and identifies multiple resources to perform the servicerequest 104 at modules 116-118. Upon identifying the multiple resources,the workload is distributed to the multiple resources. Performing theservice request at the multiple resources, the computing system maypackage these results for publication at module 226 to the servicerequestor 102.

FIG. 2B is a data diagram of an example service request 104 from aservice requestor 102. The service request 104 includes a job requestwhich is a query that specifies information. For example, the queryincludes a job request of predicting the replenishment time for aconsumable. This may include predicting when to replenish paper, ink,etc. within a printer. Additionally, the service request 104 includes acomponent identifier which specifies which particular component thequery is seeking information about. For example, the query is directedtowards the specific printer identification to determine when toreplenish the consumable for that printer. Further, the service request104 includes the latency which specifies in which period of time topublish the results of the query to the service requestor 102. Forexample, the service request 104 in FIG. 2B specifies to return theresults within five minutes. In another implementation, the latencyincludes a frequency of how often to return the results.

FIG. 2C is a block diagram of an example workload 112 as input toconvert into a resource profiler 228 and in return, the resourceprofiler 228 outputs a resource profile 230. The workload 112 is aquantified amount of work which should be performed to fulfill theservice request 104. For example, the workload 112 may include an amountof relevant data retrieved and filtered through based on the relevancyto the service request 104. The workload 112 is used as input to theresource profiler 228 to obtain the resource profile 230.Implementations of resource profiler 228 include, by way of example, aninstruction, set of instructions, algorithm, process, operation, logic,technique, function, firmware, and/or software executable by aprocessing component within the computing system of FIG. 2A.

The resource profiler 228 includes computing, capability andcharacteristics of each of the multiple resources. Thus, the resourceprofiler 228 converts the workload and uses the computing capabilitiesof each of the multiple resources to return the resource profile 230.The resource profile 230 is a computing performance profile for aparticular workload 112. The resource profile 230 includes a set of datapoints which defines a relationship of a quantity and/or type ofresources versus the latency. This relationship may be expressed as acurve or other, type of graph. This enables a correlation based on theparticular workload 112 between a number/type of resources and varyingpoints of latency. For example, one axis includes a number and/or typeof resources and another axis includes the various latencies. Thus, thecomputing system may apply the latency specified within the servicerequest 104 to filter out which of the number and/or types of resourceswould not meet the latency requirement.

FIG. 3 is a flowchart of an example method to receive a service requestincluding a latency and computing a workload for performing the servicerequest. The method identifies multiple resources to perform the servicerequest and publishes a result of the service request to a servicerequester. In discussing FIG. 3, references may be made to thecomponents in FIGS. 1-2B to provide contextual examples. For example, acomputing system which may include a scheduling module and resourceprofiler executes operations 302-310 to receive the request includingthe latency and performing the service request in compliance with thelatency. Computing the workload and identifying which of the resourcesare to receive the workload for performing the service request enablesthe computing system to adjust the workload to various resources toperform the service request to comply with the latency. As such, thecomputing device includes, by way of example, a processing unit, acontroller, a processor, a virtual machine as part of a computingsystem, etc. Further, although FIG. 5 is described as implemented by thecomputing device, it may be executed on other suitable components. Forexample, FIG. 5 may be implemented in the form of executableinstructions on as machine-readable storage medium 604 as in FIG. 6.

At operation 302, the computing system receives the service request. Theservice request may be submitted over networking services and as suchmay be submitted using JavaScript Object Notation (JSON) extensiblemarkup language (XML), or other type of format enabling the computingsystem to read the service request. The service request includes a jobrequest, a latency, and/or identifying what component the job request isdirected towards. The job request is a type of query about an aspect ofthe computing system while the identifier specifies which particularcomponent the job request is seeking information about. For example, thequery may request when a particular printer may run out of ink, thus theidentifier may include the printer identification. At operation 302, thecomputing system creates a clock signal corresponding to the latencyreceived as part of the service request The clock signal determines whenthe computing system retrieves data from a data storage area forfiltering for the data relevant to the service request. Additionally,operation 302 includes determining the filtering scheme for processingthe retrieved data to identify the relevant data. In thisimplementation, upon receiving the service request, the computing systemassigns a relevancy score to different types of data. For example, ifthe service request pertains to a query about ink, then a column of datapertaining to paper usage is not as relevant and would be assigned alower relevancy score than a column pertaining to how often a printermay complete print jobs.

At operation 304, the computing system computes the workload forperforming the service request received, at operation 302. Computing theworkload includes quantifying the workload for identifying multipleresources in which to perform the job received as the service request.The quantified workload includes data which may be retrieved andfiltered for obtaining data relevant to the service request. Thus,operation 304 may include determining an amount of the relevant data,determining a number of resources for computing the workload,determining a number of resources for performing the job in the servicerequest. In another implementation, the workload obtained from therelevant data may be expressed as a vector. Expressing the workload asthe vector takes into consideration relevant data characterizationand/or algorithm characterization for processing the relevant data. Thedata characterization includes, by way of example, a number of rows ofrelevant data, a number of columns of relevant data, and a number ofcomplex relevant data types, such as the structure, etc. The algorithmcharacterization includes, by way of example, a number of operations toscan the relevant data and or a number of operations to process therelevant data.

At operation 306, the computing system identifies the multiple resourceswhich are to perform the workload. Identifying the multiple resourcesidentifies a combination of resources, the type of resources forperforming the service request, etc. As each of the multiple resourcesmay have different computing capabilities, operation 306 includesquantifying the resources. Quantifying the resources takes into accountmultiple factors including the computing capability of processing theworkload at each of the resources and/or the quantities of relevant datafor processing the workload to perform the service request. For example,quantifying the resources may consider a number of a type of resources,a number of processing cores at each of the resources, processing clockspeed of each resource, memory assigned to each resource, and/oroperating system of each resource. Thus considering the various factors,quantifying these resources may be expressed as a tuple. The tuple is alist of ordered elements to express the various types of factors of eachof the resources. In one implementation, the computing system uses theworkload at operation 304 as input to map the workload to the variousresources. In this implementation, the computing system may obtain aperformance profile which computes a number and/or type of resourcesacross a latency period. Thus, applying the latency received atoperation 302 to the performance profile, the computing systemidentifies the multiple resources which are to perform the workload. Inthis manner, operation 306 provides art estimation for performing theservice.

At operation 308, the computing system distributes the workload computedat operation 304 to the identified multiple resources which are toperform the workload. Operation 308 may include specifying the relevantdata to those multiple resources identified at operation 306 foraccessing and processing of the relevant data to perform the servicerequest. Operation 308 may include launching that number and/or type ofresources identified at operation 306 to perform the service request. Inone implementation, the distribution of the workload may, vary fromresource to resource. For example, the computing capability for each ofthe resources may determine an amount of the workload that particulartype of resource may handle.

At operation 310, the computing system publishes the results of theservice request to the service requestor in accordance with the latency.Publishing the results includes disseminating the information of theresults to the service requestor. As such, publishing the results of theservice request include, by way of example, transmitting the results tothe service requestor, packaging results from each of the multipleresources which performed the workload, and dispatching these results tothe service request. Operation 310 may include storing the results ofthe service request within a data storage area upon receiving theresults from each of the multiple resources.

FIG. 4 is a flowchart of an example method to receive a service requestincluding a latency requirement. Upon receiving the service request, themethod retrieves heterogeneous data for filtering to obtain datarelevant to the service request. The method may proceed to determine anamount of the relevant data for computing the workload. Upon computingthe workload, the method identifies which resources from among multipleresources are to perform the service request. The method identifies themultiple resources by mapping the workload to multiple resources andapplies the latency requirement to the multiple resources for filteringwhich of the resources among the multiple resources are to, perform theworkload so the service request is performed within, the latencyrequirement. In discussing FIG. 4, references may be made to thecomponents in FIGS. 1-2B to provide contextual examples. For example, acomputing system may include a scheduling module and resource profilerexecutes operations 402-420 to receive the service request andprocessing heterogeneous data to obtain the relevant data foridentifying which multiple resources are to process the relevant datafor performing the service request. As such, the computing deviceincludes, by way of example, a processing unit, a controller, aprocessor, a virtual machine as part of a computing system, etc.Further, although FIG. 4 is described as implemented by the computingdevice, it may be executed on other suitable components. For example,FIG. 4 may be implemented in the thrill of executable instructions on amachine-readable storage medium 604 as in FIG. 6.

At operation 402, the computing system receives the service requestincluding the latency requirement. The latency specifies a window oftime in which to perform a job within the service request. As such, thecomputing system may consider the workload of the job and computingcapabilities of the resources to ensure the service request is performedby the latency requirement. Operation 402 may be similar infunctionality to operation 302 as in FIG. 3.

At operation 404, the computing system retrieves heterogeneous data froma data storage. The data storage may receive data of unknown format andfrom an unknown number of resources. Thus to process the heterogeneousdata to obtain the data relevant to the service request, the computingsystem retrieves the heterogeneous data. In one implementation, uponreceiving the service request at operation 402, the computing systemsets a clock signal corresponding to the latency provided with theservice request. This clock signal provides guidance for beginning aretrieval of the heterogeneous data. In another implementation, thecomputing system retrieves the most recent heterogeneous data from thedata storage. In this implementation, the computing system may trackwhich data may have already been retrieved in relation to a differentservice request. Thus, the computing system retrieves the data which hasnot yet been retrieved from the data storage.

At operation 406, the computing system filters the retrievedheterogeneous data to obtain data relevant to the service request. Theservice request includes data in which to identify the relevant datafrom the heterogeneous data. In one implementation, upon receiving theservice request, the computing system assigns relevancy scores todifferent types of data. For example, if the service request pertains toa printing service, printing data may be given a higher relevancy scorethan data regarding heuristic information about networks. Using therelevancy score, the computing system may obtain the data relevant tothe service request.

At operation 408, the computing system determines an amount of relevantdata obtained at operation 406. The amount of relevant data may includequantifying a number of columns, data sets, data rows, and/or data setsto determine the amount of relevant data. Determining the amount ofrelevant data enables the computing system to compute the workload as atoperation 410.

At operation 410, the computing system computes the workload. Computingthe workload is a way for quantifying the workload for performing, theservice request received at operation 402. In one implementation, theworkload is quantified from the amount of relevant data determined atoperation 408. Operation 410 may be similar in functionality tooperation 304 as in FIG. 3.

At operation 412, the computing system identifies multiple resourceswhich are to perform the service request received at operation 402. Inone implementation, the computing system identifies the resources fromamong the resources by mapping the workload to the multiple resourcesand applying the latency requirement to the multiple resources to filterthe various resources. Filtering the resources enables the computingsystem to identify which of the resources may handle the service requestwithin the latency. Operation 412 may be similar in functionality tooperation 306 as in FIG. 3.

At operation 414, the computing system maps the workload computed atoperation 410 to various multiple resources. In one implementation, theworkload is mapped through a performance profile. The performanceprofile includes a set of data points that includes a number, type,and/or combination of the resources on one axis and a continuing latencyon the other axis. In this manner, the computing system may apply thelatency requirement to determine the number, type, and/or combination ofresources which would meet the latency requirement.

At operation 416, the computing system applies to the latency receivedas, part of the service request at operation 402 to the mapping of theworkload to the multiple resources. The mapping at operation 414includes a range of various latency requirements to determine at whichpoint of the various latency requirements, which resources may be usedto complete workload in accordance with the various latencyrequirements. Thus, operation 416 filters out the latency requirementsthat may be greater than the latency requirement received at operation402. In doing so, the computing system also filters out the resourcesused to perform the workload at that filtered out latency. In thismanner, the computing system may identify the multiple resources priorto distributing the workload to those multiple resources as at operation418. Additionally, in this manner, the computing system takes intoaccount the amount of relevant data for processing the service request.

At operation 418, the computing system distributes the workload to eachof the resources identified at operation 412. The workload includes therelevant data as determined at operation 408, thus the computing systemmay vary the amount of relevant data to each of the resources dependingon the computing capability of each of those resources and/or the numberand types of resources allocated. For example, the resources areidentified at operation 412 and allocated at operation 418 in a manneroptimized to fulfill the latency request. This optimization considersquantities of relevant data at operation 408 and a relationship of thequantity of the resources versus latency. This enables the computingsystem to adjust the amount of workload to each of the resources to meetthe latency condition received at operation 402. Operation 418 may besimilar in functionality to operation 308 as in FIG. 3.

At operation 420, the computing system publishes the results of theservice request to the service requestor. The publication of the resultsoccurs within the latency period as defined at operation 302. Thepublication of the results includes disseminating the results of theperformance of the service request to the service requestor. Operation420 may be similar in functionality to operation 310 as in FIG. 3.

FIG. 5 is flowchart of an example method to receive a service requestincluding a latency. Upon receiving the service request, the methodcomputes a workload for performing the service request and identifiesmultiple resources which are to perform the workload. Identifying themultiple resources may include representing the workload as a vector forinput to obtain a resource profile. Obtaining the resource profile, themethod may apply the latency to the profile to determine which of themultiple resources are to perform the service request. Identifying themultiple resources, the method may proceed to distribute the workload tothe identified multiple resources and publish results of the servicerequest. In discussing FIG. 5, references may be made to the componentsin FIGS. 1-2B to provide contextual examples. For example, a computingsystem which may include a scheduling module and resource profilerexecutes operations 502-516 to distribute the workload to multipleresources for publishing a result of the service request. As such, thecomputing device includes, by way of example, a processing unit, acontroller, a processor, a virtual machine as part of a computingsystem, etc. Further, although FIG. 5 is described as implemented by thecomputing device, it may be executed on other suitable components. Forexample, FIG. 5 may be implemented in the form of executableinstructions on a machine-readable storage medium 604 as in FIG. 6.

At operation 502, the computing system receives the service requestincluding the latency. The latency is a window of time to instruct thecomputing system of when to publish the results of the service request.For example, the latency may include 10 minutes which instructs an upperbound of time in which the computing system should publish results. Inthis example, the latency of 10 minutes means for the computing systemto publish the results within 10 minutes. Operation 502 may be similarin functionality to operations 302 and 402 as in FIGS. 3-4.

At operation 504, the computing system computes the workload forperforming the service request. As explained earlier, computing theworkload includes quantifying the workload. As such, quantifying theworkload includes an amount of relevant data which may be processed toperform the service request. Prior to operation 504, the relevant datamay be retrieved and filtered according to a relevancy of the data tothe service request received at operation 502. Computing the workloadfor the performance of the service request may include, by way ofexample, determining a number of the multiple resources for performingthe service request, determining a computing capability of each of theresources, determining a type of the multiple resource, etc. In oneimplementation, computing the workload includes representing theworkloads as a vector as at operation 508. Operation 504 may be similarin functionality to operations 304 and 410 as in FIGS. 3-4.

At operation 506, the computing system identifies which of the multipleresources are to perform the service request. Identifying the multipleresources based on the workload computed at operation 504 enables thecomputing system to estimate the computing capability and/or allowanceof each of the resources to ensure the service request is performedwithin the latency period. In one implementation, the computing systemidentifies the multiple resources through representing the workload as avector, using the workload vector as input to obtain the resourceprofile, and applying the latency to the resource profile to filter outtypes of resources, a number of resources, and/or combination ofresources such as at operation 508-512. Operation 506 may be similar infunctionality to operations 306 and 412 as in FIGS. 3-4.

At operation 508, the computing system expresses the workload as thevector. The vector is an expression of the workload for representing thevarious elements of the workload. Expressing the workload as enables thecomputing system to quantify the workload with these various elementsand/or characterizations. For example, these various elements and/orcharacterizations may include relevant data characterization and/oralgorithm characterization for processing the relevant data. The datacharacterization includes, by way of example, a number of rows ofrelevant data, a number of columns of relevant data, and a number ofcomplex relevant data types, such as the structure, etc. The algorithmcharacterization includes, by way of example, a number of operations toscan the relevant data and/or a number of operations to process therelevant data. The workload vector may be used as input to obtain theresource profile as at operation 510.

At operation 510, the computing system obtains the resource profilebased on the workload vector. The resource profile is a set of datapoints which shows which types and/or number of resources may fulfillthe latency requirement. Specifically, the resource profile converts theworkload vector into a resource and latency pair. For example, theresource profile max include a curve on which one axis may include anumber and/or type of resources while the other axis includes acontinuation of latency that extends past the latency requirement.Providing the resource profile enables the computing system to apply thelatency requirement received at operation 502 to determine which of thenumber and/or type of resources would perform the service request withinthe latency requirement. The application of the latency to filter outwhich of the resources should be used for performing the service requestoccurs at operation 512.

At operation 512, the computing system applies the latency requirementto the resource profile to identify the number, combination and/or typesof resources should perform the service request within the latencyrequirement. Upon identifying the multiple resources, the computingsystem proceeds to operations 514-516.

At operation 514, the computing system distributes the workload to themultiple resources identified at operation 506. The distribution of theworkload may vary to each of the multiple resources depending on thecomputing capability for each of those resources. For example, oneparticular resource may be able to process and/or compute the data ofthe workload than another resource. Thus operation 512 adjusts theworkload to each of the multiple resources including the number ofresources and/or type of resources. Adjusting the workload takes intoaccount to the latency to ensure the service request is performed withinthe latency period established by the service request. Operation 514 maybe similar in functionality to operations 308 and 418 as in FIGS. 3-4.

At operation 516, the results of the service request are published tothe service requester. Publishing the results includes disseminating theinformation of the results to the service requester. As such, publishingthe results of the service request include, by way of example,transmitting the results to the service requester, packaging resultsfrom each of the multiple resources which performed the workload, anddispatching these results to the service request. Operation 516 mayinclude storing the results of the service request within a data storagearea upon receiving the results from each of the multiple resources.Operation 516 may be similar in functionality to operations 310 and 420as in FIGS. 3-4.

FIG. 6 is a block diagram of computing device 600 with a processor 602to execute instructions 606-626 within a machine-readable storage medium404. Specifically, the computing device 600 with the processor 602receives a service request including a latency, computes a workload toperform the service request, and identifies multiple resources toperform the workload based on computing capability of each of themultiple resources. Upon identifying the multiple resources, theprocessor 602 may proceed to distribute the workload and then publishresults of the service request. Although the computing device 600includes processor 602 and machine-readable storage medium 604, it mayalso include other components that would be suitable to one skilled inthe art. For example, the computing device 600 may include a datastorage area as in FIG. 2A. The computing device 600 is an electronicdevice with the processor 602 capable of executing instructions 606-626,and as such embodiments of the computing device 600 include a computingdevice, mobile device, client device, personal computer, desktopcomputer, laptop, tablet, video game console, or other type ofelectronic device capable of executing instructions 606-626. Theinstructions 606-626 may be implemented as methods, functions,operations, and other processes implemented as machine-readableinstructions stored on the storage medium 604, which may benon-transitory, such as hardware storage devices (e.g., random accessmemory (RAM), read only memory (ROM), erasable programmable ROM,electrically erasable ROM, hard drives, and flash memory.

The processor 602 may fetch, decode, and execute instructions 606-626 toreceive the service request including the latency and identify multipleresources based on the latency to perform a workload from servicerequest and publish results of the service request. In oneimplementation, upon executing instruction 606, the processor 602 mayexecute instruction 608 through executing instruction 610-616. Inanother implementation, upon executing instructions 606-608, theprocessor 602 may execute instruction 618 by performing a combination ofinstructions 620-622. In a further implementation, upon executinginstruction 618, the processor 602 may proceed to execute instructions624-626. Specifically, the processor 602 executes instructions 606-616to receive the service request including the latency defining a timelimit of when to return results of the service request; compute theworkload for performing the service request; identify relevant data tothe service request; determine an amount of the relevant data to theservice request; express the amount of relevant data as a workload; anddetermine a number of multiple resources for performing the servicerequest. The processor 602 may proceed to execute instructions 618-622to: identify the multiple resources; obtain a resource profileexpressing the number and/or combination of the multiple resources in acontinuing latency time frame; and using the latency received with theservice request, apply the latency to the resource profile to determinethe combination of the multiple resources for performing the servicerequest. The process 602 may then proceed to execute instructions624-626 to: distribute the workload for performing the service requestto the identified multiple resources; and publish the results of theservice request performed at the identified multiple resources.

The machine-readable storage medium 604 includes instructions 606-626for the processor 602 to fetch, decode, and execute. In anotherembodiment, the machine-readable storage medium 604 may be anelectronic, magnetic, optical, memory, storage, flash-drive, or otherphysical device that contains or stores executable instructions. Thus,the machine-readable storage medium 604 may include, for example, RandomAccess Memory (RAM), an Electrically Erasable Programmable Read-OnlyMemory (EEPROM), a storage drive, a memory cache, network, storage, aCompact Disc Read Only Memory (CDROM) and the like. As such, themachine-readable storage medium 604 may include an application and/orfirmware which can be utilized independently and/or in conjunction withthe processor 602 to fetch, decode, and/or execute instructions of themachine-readable storage medium 604. The application and/or firmware maybe stored on the machine-readable storage medium 604 and/or stored onanother location of the computing device 600.

Thus, examples disclosed herein provide an efficient mechanism tobalance and adjust workloads to various resources to ensure aperformance of a service request within specified latency.

We claim:
 1. A method for scheduling a service request, the methodcomprising: receiving the service request including a latency associatedwith a publication of a result of the service request; retrievingheterogeneous data upon the receipt of the service request; filteringthe heterogeneous data to obtain data relevant to the service request;determining an amount of relevant data prior to computing a workload forthe service request; computing the workload for the service request;identifying multiple resources, based on the latency and the workload,to perform the service request; distributing the workload to theidentified multiple resources; and publishing the results of the servicerequest in accordance with the latency.
 2. The method of claim 1 whereincomputing the workload comprises: identifying the data relevant to theservice request; and determining the amount of the relevant data.
 3. Themethod of claim 1 wherein identifying the multiple resources, based onthe latency and the workload, to perform the service request comprises:representing the workload as a vector; obtaining a resource profile ofthe multiple resources for the workload; and applying the latency to theresource profile to identify the multiple resources for performing theservice request.
 4. The method of claim 1 wherein identifying themultiple resources, based on the latency and the workload, to performthe service request comprises mapping the workload to multipleresources; and applying the latency to the multiple resources foridentifying the multiple resources to perform the service request. 5.The method of claim of claim 1 wherein identified multiple resourcesinclude multiple virtual machines with different computing capabilities.6. A system comprising a processor to: receive a service requestincluding a latency associated with a publication of a result of theservice request; retrieve heterogeneous data; filter the heterogeneousdata for obtaining data relevant to a service request; compute aworkload for the service request; determine a workload capability foreach of multiple resources; and identify the multiple resources that areto perform the service request by applying the latency to the multipleresources, wherein the relevant data is represented as the workload foridentifying the multiple resources.
 7. The system of claim 6, whereinthe processor is to publish results of the service results in accordancewith the latency.
 8. The system of claim 6, wherein the processor is todistribute the workload to the identified multiple resources, thedistribution of the workload is different for each of the identifiedmultiple resources.
 9. The system of claim 6 comprising: a data storageto maintain heterogeneous data for retrieval by the scheduling modulefor computing the workload.
 10. A non-transitory machine-readablestorage medium comprising instructions that when executed by aprocessing resource cause a computing device to: retrieve heterogeneousdata; filter the heterogeneous data for obtaining data relevant to aservice request; compute a workload for the service request, wherein theservice request includes a latency associated with a publication ofresult of the service request; identify multiple resources, based on thelatency and the workload, to perform the service request, wherein therelevant data is represented as the workload for identifying themultiple resources; distribute the workload to the identified multipleresources, wherein the distribution of the workload is different foreach of the identified multiple resources; and publish the results ofthe service request in accordance with the latency.
 11. Thenon-transitory machine-readable storage medium of claim 10 comprisinginstructions that when executed by the processing resource cause thecomputing device to: receive the service request.
 12. The non-transitorymachine-readable storage medium of claim 10 wherein computing theworkload is comprising instructions that when executed by the processingresource causes the computing device to: identify the data relevant tothe service request; determine an amount of the relevant data; andexpress the relevant data as a vector of the workload; and determine anumber of the multiple resources to perform the workload.
 13. Thenon-transitory machine-readable storage medium of claim 10 whereinidentifying the multiple resources, based on the latency and theworkload, to perform the service request is comprising instructions thatwhen executed by the processing resource causes the computing device to:obtain a resource profile for workload, the resource profile includesthe multiple resources; and determine a combination of the multipleresources for identifying the multiple resources to perform the servicerequest based on the latency.